Analytical Hierarchy Process and Simple Additive Weighting Methods for Ranking Public Complaint Applications with ISO/IEC 25010
DOI:
https://doi.org/10.31849/digitalzone.v17i1.26991Keywords:
AHP, ISO/IEC 25010, Public Complaint Applications, Rank, SAWAbstract
An integrated decision support framework for evaluating and ranking public complaint applications operated by city governments in West Java Province, Indonesia, is presented in this study. The Analytical Hierarchy Process is employed to derive expert-based weights for the ISO/IEC 25010 software quality criteria, while the Simple Additive Weighting method is applied to generate objective rankings of the applications. A research gap in the e-government domain is addressed, as limited studies have systematically implemented a multi-criteria decision-making approach that integrates expert consensus with quantitative evaluation for prioritizing digital public services. Data were collected through expert panel assessments and structured questionnaires administered across nine city-level complaint systems. Significant variations in software quality among the evaluated applications were identified, with Depok and Bandung achieving the highest scores of 0.8053 and 0.7593, respectively, while Bekasi and Cimahi obtained the lowest scores. Specific recommendations for improvement are provided based on the eight quality dimensions defined in the ISO/IEC 25010 model, and a replicable evaluation model for government stakeholders is proposed. A theoretical contribution is established through the extension of multi-criteria decision-making applications in evaluating e-government software quality using the comprehensive ISO/IEC 25010 framework. A practical contribution is delivered through the provision of a structured benchmarking tool that enables local governments to prioritize improvements in digital complaint systems based on measurable software quality indicators
References
[1] J. J. P. Latupeirissa, N. L. Y. Dewi, I. K. R. Prayana, M. B. Srikandi, S. A. Ramadiansyah, and I. B. G. A. Y. Pramana, “Transforming Public Service Delivery: A Comprehensive Review of Digitization Initiatives,” Sustain. , vol. 16, no. 7, 2024, http://doi.org/10.3390/su16072818.
[2] Z. Irani, R. M. Abril, V. Weerakkody, A. Omar, and U. Sivarajah, “The impact of legacy systems on digital transformation in European public administration: Lesson learned from a multi case analysis,” Gov. Inf. Q., vol. 40, no. 1, p. 101784, 2023, http://doi.org/10.1016/j.giq.2022.101784.
[3] B. D. McDonald, J. L. Hall, J. O’Flynn, and S. van Thiel, “The future of public administration research: An editor’s perspective,” Public Adm., vol. 100, no. 1, pp. 59–71, 2022, http://doi.org/10.1111/padm.12829.
[4] J. Widodo and K. Kusnan, “Mapping and Analyzing E-Government Sentiments in Local Governments in Indonesia,” Society, vol. 11, no. 2, pp. 434–457, 2023, http://doi.org/10.33019/society.v11i2.558.
[5] P. Radika, R. Rachmawati, and R. Rijanta, “Analysing smart governance in public service delivery: evidence from 61 smart cities in Indonesia,” Hum. Geogr., vol. 19, no. 2, 2025, http://doi.org/10.5719/hgeo.2025.192.5.
[6] N. Xiong, H. Zang, H. Lu, R. Yu, J. Wang, and Z. Feng, “Performance Analysis of Smart City Governance: Dynamic Impact of Beijing 12345 Hotline on Urban Public Problems,” Sustain., vol. 14, no. 16, 2022, http://doi.org/10.3390/su14169986.
[7] F. Shama, A. Aziz, and L. B. M. Deya, “CitySolution: A complaining task distributive mobile application for smart city corporation using deep learning,” SoftwareX, vol. 27, no. May, p. 101829, 2024, http://doi.org/10.1016/j.softx.2024.101829.
[8] D. Bastos, A. Fernández, A. Pereira, and N. Pacheco, “Aplicaciones de las ciudades inteligentes para promover la participación ciudadana en la gestión y gobernanza de las ciudades.,” Publ. Open Access Journals, vol. 9, no. 4, pp. 1–29, 2022, [Online]. Available: https://www.mdpi.com/2227-9709/9/4/89
[9] X. Sun, B. Yu, and R. Li, “Designing an innovative Multi-Criteria Decision Making (MCDM) framework for optimized teaching and delivery of physical education curriculum,” Sci. Rep., vol. 15, no. 1, pp. 1–21, 2025, http://doi.org/10.1038/s41598-025-14283-7.
[10] M. A. Alsalem et al., Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review, vol. 55, no. 6. Springer Netherlands, 2022. http://doi.org/10.1007/s10462-021-10124-x.
[11] B. Kizielewicz, A. Shekhovtsov, and W. Sałabun, “pymcdm—The universal library for solving multi-criteria decision-making problems,” SoftwareX, vol. 22, p. 101368, 2023, http://doi.org/10.1016/j.softx.2023.101368.
[12] M. Claessens et al., “Quality Assurance for AI-Based Applications in Radiation Therapy,” Semin. Radiat. Oncol., vol. 32, no. 4, pp. 421–431, 2022, http://doi.org/10.1016/j.semradonc.2022.06.011.
[13] J. Gyani, A. Ahmed, and M. A. Haq, “MCDM and Various Prioritization Methods in AHP for CSS: A Comprehensive Review,” IEEE Access, vol. 10, pp. 33492–33511, 2022, doi: 10.1109/ACCESS.2022.3161742.
[14] G. H. Djatmiko, O. Sinaga, and S. Pawirosumarto, “Digital Transformation and Social Inclusion in Public Services: A Qualitative Analysis of E-Government Adoption for Marginalized Communities in Sustainable Governance,” Sustain., vol. 17, no. 7, pp. 1–28, 2025, doi: 10.3390/su17072908.
[15] M. Cosa and R. Torelli, “Digital Transformation and Flexible Performance Management: A Systematic Literature Review of the Evolution of Performance Measurement Systems,” Glob. J. Flex. Syst. Manag., vol. 25, no. 3, pp. 445–466, 2024, doi: 10.1007/s40171-024-00409-9.
[16] P. Parycek, V. Schmid, and A. S. Novak, “Artificial Intelligence (AI) and Automation in Administrative Procedures: Potentials, Limitations, and Framework Conditions,” J. Knowl. Econ., vol. 15, no. 2, pp. 8390–8415, 2024, doi: 10.1007/s13132-023-01433-3.
[17] H. Hokmabadi, S. M. H. S. Rezvani, and C. A. de Matos, “Business Resilience for Small and Medium Enterprises and Startups by Digital Transformation and the Role of Marketing Capabilities—A Systematic Review,” Systems, vol. 12, no. 6, 2024, doi: 10.3390/systems12060220.
[18] M. Alojail and S. B. Khan, “Impact of Digital Transformation toward Sustainable Development,” Sustain., vol. 15, no. 20, pp. 1–20, 2023, doi: 10.3390/su152014697.
[19] T. D. N. Vuong and L. T. Nguyen, “The Key Strategies for Measuring Employee Performance in Companies: A Systematic Review,” Sustainability, vol. 14, no. 21, p. 14017, 2022, doi: 10.3390/su142114017.
[20] H. Idrees, J. Xu, S. A. Haider, and S. Tehseen, “A systematic review of knowledge management and new product development projects: Trends, issues, and challenges,” J. Innov. Knowl., vol. 8, no. 2, p. 100350, 2023, doi: 10.1016/j.jik.2023.100350.
[21] S. Başaran, “Hybrid Fuzzy–Rough MCDM Framework and Decision Support Application for Sustainable Evaluation of Virtualization Technologies,” Appl. Syst. Innov., vol. 9, no. 2, 2026, doi: 10.3390/asi9020034.
[22] J. Miliauskaitė, A. Slotkienė, and P. Lėveris, “User-Aware Trust Evaluation of Web Services: A Fuzzy-Based MCDM Approach,” Appl. Sci., vol. 16, no. 1, 2026, doi: 10.3390/app16010141.
[23] Y. Li, Y. Bian, Z. Zhang, S. Zhao, and Y. Liu, “CTHP: Selection for adoption of open-source bioinformatics software based on a customised ISO 25010 quality model, three-way decision and Delphi hierarchy process,” IET Softw., vol. 17, no. 4, pp. 496–508, 2023, doi: 10.1049/sfw2.12134.
[24] B. L. Golden and E. A. Wasil, The Analytic Hierarchy Process. 1989. doi: 10.1007/978-3-642-50244-6.
[25] E. Turban, J. E. Aronson, and T. P. Liang, Decision Support and Intelligrnt System, Seventh Ed. New Delhi: Prentice-hall, 2007.
[26] M. Izzatillah, “Quality Measurement of Transportation Service Application Go-Jek Using Iso 25010 Quality Model,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 10, no. 1, pp. 233–242, 2019, doi: 10.24176/simet.v10i1.2945.
[27] D. J. Power, Decision Support, Analytics, and Business Intelligence Second Edition The Information Systems Collection. 2002.
[28] B. Brewer, “Citizen or customer? Complaints handling in the public sector,” Int. Rev. Adm. Sci., vol. 73, no. 4, pp. 549–556, 2007, doi: 10.1177/0020852307083457.
[29] V. Aprilina, T. Dompak, L. Salsabila, K. T. Lodan, and U. P. Batam, “The Role Of Digitalization In Enhancing Public Service Efficiency : Challenges And Opportunities In Managing Public Complaints Through E- Government In Indonesia,” Int. J. Soc. Welf. Fam. Law, 2025, doi: https://doi.org/10.62951/ijsw.v2i1.183.
[30] T. Montanaro, I. Sergi, M. Basile, L. Mainetti, and L. Patrono, “An IoT-Aware Solution to Support Governments in Air Pollution Monitoring Based on the Combination of Real-Time Data and Citizen Feedback,” Sensors, vol. 22, no. 3, 2022, doi: 10.3390/s22031000.
[31] L. A. Glasenapp, A. F. Hoppe, M. A. Wisintainer, A. Sartori, and S. F. Stefenon, “OCR Applied for Identification of Vehicles with Irregular Documentation Using IoT,” Electron., vol. 12, no. 5, pp. 1–15, 2023, doi: 10.3390/electronics12051083.
[32] R. Edwards, V. Gillies, and S. Gorin, “Problem-solving for problem-solving: Data analytics to identify families for service intervention,” Crit. Soc. Policy, vol. 42, no. 2, pp. 265–284, 2022, doi: 10.1177/02610183211020294.
[33] A. Aldoseri, K. N. Al-Khalifa, and A. M. Hamouda, “AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact,” Sustain. , vol. 16, no. 5, 2024, doi: 10.3390/su16051790.
[34] R. Castilla, A. Pacheco, and J. Franco, “Digital government: Mobile applications and their impact on access to public information,” SoftwareX, vol. 22, p. 101382, 2023, doi: 10.1016/j.softx.2023.101382.
[35] M. Ghamari, P. Rangel, M. Mehrubeoglu, G. S. Tewolde, and R. Simon Sherratt, “Unmanned Aerial Vehicle Communications for Civil Applications: A Review,” IEEE Access, vol. 10, no. September, pp. 102492–102531, 2022, doi: 10.1109/ACCESS.2022.3208571.
[36] K. Sarieddine, M. A. Sayed, S. Torabi, R. Atallah, and C. Assi, “Investigating the Security of EV Charging Mobile Applications as an Attack Surface,” ACM Trans. Cyber-Physical Syst., vol. 7, no. 4, 2023, doi: 10.1145/3609508.
[37] Y. Xia et al., “Sustainable service quality assessment of Chinese healthcare e-government: a multi-criteria decision framework based on SERVQUAL model and entropy-weight TOPSIS method,” Front. Digit. Heal., vol. 7, no. September, pp. 1–20, 2025, doi: 10.3389/fdgth.2025.1611979.
[38] B. J. Tiika, Z. Tang, J. Azaare, J. C. Dagadu, and S. N. A. Otoo, “Evaluating E-Government Development among Africa Union Member States: An Analysis of the Impact of E-Government on Public Administration and Governance in Ghana,” Sustain., vol. 16, no. 3, 2024, doi: 10.3390/su16031333.
[39] V. G. de Menezes, G. V. Pedrosa, M. P. P. da Silva, and R. M. d. C. Figueiredo, “Evaluation of Public Services Considering the Expectations of Users—A Systematic Literature Review,” Inf., vol. 13, no. 4, pp. 1–13, 2022, doi: 10.3390/info13040162.
[40] Y. Argotti, Y. Kenfaoui, C. Baron, A. Abran, and P. Esteban, “An Operational Quality Model of Embedded Software Aligned with ISO 25000,” ACM Trans. Embed. Comput. Syst., vol. 24, no. 1, 2024, doi: 10.1145/3691642.
[41] H. Rojas, R. Renteria, and V. Martinez, “Understanding Performance Efficiency in ISO/IEC 25000: A Systematic Literature Review,” Int. J. Informatics Vis., vol. 9, no. 6, pp. 2405–2417, 2025, doi: 10.62527/joiv.9.6.3074.
[42] J. Ferdous, F. Bensebaa, A. S. Milani, K. Hewage, P. Bhowmik, and N. Pelletier, “Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review,” Sustain. , vol. 16, no. 7, 2024, doi: 10.3390/su16072684.
[43] M. Madanchian and H. Taherdoost, “Applications of Multi-Criteria Decision Making in Information Systems for Strategic and Operational Decisions,” Computers, vol. 14, no. 6, pp. 1–21, 2025, doi: 10.3390/computers14060208.
[44] S. Frish, I. Talmor, O. Hadar, M. Shoshany, and A. Shapira, “Enhancing consistency of AHP-based expert judgements: A new approach and its implementation in an interactive tool,” MethodsX, vol. 14, no. April, p. 103341, 2025, doi: 10.1016/j.mex.2025.103341.
[45] M. T. Al Fozaie and H. Wahid, “A Guide to Integrating Expert Opinion and Fuzzy AHP When Generating Weights for Composite Indices,” Adv. Fuzzy Syst., vol. 2022, 2022, doi: 10.1155/2022/3396862.
[46] A. E. Ionașcu, S. S. Goswami, A. Dănilă, M. G. Horga, C. A. Barbu, and A. Şerban-Comǎnescu, “Analyzing Primary Sector Selection for Economic Activity in Romania: An Interval-Valued Fuzzy Multi-Criteria Approach,” Mathematics, vol. 12, no. 8, 2024, doi: 10.3390/math12081157.
[47] S. Busra Ayan, M. Abacioglu, and P. Basilio, “A Comprehensive Review of the Novel Weighting Methods for,” Information, vol. 14, no. 5, p. 285, 2023, doi: https://doi.org/10.3390/ info14050285.
[48] E. S Al- Tarawneh and L. kh Al-Tarawneh, “Introducing Comprehensive Software Quality Model for Evaluating and Development E-Government Websites in Jordan Using Fuzzy Analytical Hierarchy Process,” Webology, vol. 19, no. 1, pp. 890–902, 2022, doi: 10.14704/web/v19i1/web19061.
[49] H. Muhammad and M. Hromada, “Evaluating a Proposed E-Government Stage Model in Terms of Personal Data Protection,” Appl. Sci., vol. 13, no. 6, 2023, doi: 10.3390/app13063913.
[50] L. Souza-Pereira, N. Pombo, and S. Ouhbi, “Software quality: Application of a process model for quality-in-use assessment,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 7, pp. 4626–4634, 2022, doi: 10.1016/j.jksuci.2022.03.031.
[51] T. L. Saaty and L. G. Vargas, Models, Methods, Concepts & Applications of the Analytic Hierarchy Process, no. July. 2012. doi: 10.1016/b978-0-08-032599-6.50008-8.
[52] G.-H. T. J.-J. Huang, Multiple Attribute Decision Making Methods and Application. 2011.
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